Computer Vision in HealthCare Application

LAB-3

Name: Sarvesh Sridhar

Reg.No: 19BAI1057

Dataset used: Skin Cancer HAM10000

Link: https://www.kaggle.com/surajghuwalewala/ham1000-segmentation-and-classification

Lab Description:

Experimentation of Edge Detection Techniques


Sobel Edge Detection

Canny Edge Detection

Prewitt Edge Detection

Laplacian Edge Detection

Noise Image

Sobel

Canny

Prewitt

Laplacian

Conclusion:

Sobel Edge Detection technique detects edges better for color image than grayscale image.

Canny Edge Detection technique detects edges better for colour image than grayscale image (comparing results with naked eye)

Prewitt Edge Detection technique gives good result with both color and grayscale image but comparing it with naked eye, canny can be said to perform better. It gives off a glossy shade/elevation over the edges and its interior in our image.

Laplacian Edge Detection gave pixel like output for color image and very light output for grayscale image.

We have tried experimenting it with Gaussian Noised image. It is observed that sobel x-axis and y-axis kernel perform slightly better than the rest of the techniques as we can see a very light edges in the output.

So for Grayscaled imgae, sobel (x-axis, y-axis kernels), canny, Prewitt performs better. For color image, sobel (x-axis, y-axis kernels), canny, Prewitt performs better. For noisy image, sobel (x-axis,y-axis kernels) produces some output as compared to rest.